排灌机械工程学报
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排灌机械工程学报  2018, Vol. 36 Issue (8): 762-766    DOI: 10.3969/j.issn.1674-8530.18.1042
泵理论与技术 最新目录 | 下期目录 | 过刊浏览 | 高级检索 Previous Articles  |  Next Articles  
BP神经网络与GA-BP农作物需水量预测模型对比
江显群, 陈武奋*
珠江水利委员会珠江水利科学研究院, 广东 广州 510610
Comparison between BP neural network and GA-BP crop water demand forecasting model
JIANG Xianqun, CHEN Wufen*
Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou, Guangdong 510610, China
 全文: PDF (1823 KB)   HTML (1 KB)   输出: BibTeX | EndNote (RIS)      背景资料
摘要 农作物需水量预测是制定合理灌溉制度的重要依据.针对BP神经网络的不足,利用遗传算法(GA)具有全局搜索能力强的特点,建立基于GA-BP神经网络的农作物需水量预测模型.以广州辣木农庄试验田农作物作为研究对象,结果表明:基于BP神经网络农作物需水量预测模型测试集均方误差和确定性系数分别为0.037和0.648;GA-BP神经网络农作物需水量预测模型测试集均方误差和确定性系数分别为0.013和0.882,GA-BP农作物需水量预测模型收敛速度、确定性系数和性能均优于BP农作物需水量预测模型.
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江显群
陈武奋*
关键词农作物需水量   节水灌溉   遗传算法   BP神经网络   预测模型     
Abstract: Forecasting crop water requirement is an important basis for formulating a reasonable irrigation system. In view of the deficiencies of BP neural network, genetic algorithm(GA)has the characteristics of strong global search capability, and a prediction model of crop water requirement based on GA-BP neural network was established. Taking the experimental crops of the Lamu Farm in Guangzhou as the object of research, the results show that the mean square error and certainty coefficient of the test set of crop water requirement prediction model based on BP neural network are 0.037 and 0.648 respectively. The square error and the certainty coefficient of the rest set of crop water demand forecasting model based on the GA-BP neural network are 0.013 and 0.882 resiectively. The GA-BP crop water demand forecasting model has a convergence rate, certainty coefficient and performance better than the BP crop water demand forecasting model.
Key wordswater requirement for crops   water-saving irrigation   genetic algorithm   BP neural network   forecasting model   
收稿日期: 2018-04-24;
基金资助:

广州市科技计划项目(201604020049)

引用本文:   
江显群,陈武奋*. BP神经网络与GA-BP农作物需水量预测模型对比[J]. 排灌机械工程学报, 2018, 36(8): 762-766.
JIANG Xian-Qun,CHEN Wu-Fen-*. Comparison between BP neural network and GA-BP crop water demand forecasting model[J]. Journal of Drainage and Irrigation Machinery Engin, 2018, 36(8): 762-766.
 
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